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@faun shared a link, 1 month ago

walrus: ingesting data at memory speeds

Walrusis a lock-free, single-nodeWrite Ahead Log in Rustthat rips through a million ops/sec and moves 1 GB/s of write bandwidth - on bare-metal, nothing fancy. It leans on mmap-backed sparse files, atomic counters, and zero-copy reads to get there. Each topic gets its own line of 10MB memory-mapped ..

walrus: ingesting data at memory speeds
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@faun shared a link, 1 month ago

OpenAI Agent Builder: A Complete Guide to Building AI Workflows Without Code

OpenAI’sAgent Builderdrops the guardrails. It’s a no-code, drag-and-drop playground for building, testing, and shipping AI workflows - logic flows straight from your brain to the screen. Tweak interfaces inWidget Studio. Plug into real systems with theAgents SDK. Just one catch: it’s locked behind P..

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@faun shared a link, 1 month ago

Technical Tuesday: 10 best practices for building reliable AI agents in 2025

UiPath just droppedAgent Builder in Studio- a legit development environment for AI agents that can actually handle enterprise chaos. Think production-grade: modular builds, traceable steps, and failure handling that doesn’t flake under pressure. It’s wired forschema-driven prompts,tool versioning, a..

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@faun shared a link, 1 month ago

Write Deep Learning Code Locally and Run on GPUs Instantly

Modal cuts the drama out of deep learning ops. Devs write Python like usual, then fire off training, eval, and serving scripts to serverless GPUs - zero cluster wrangling. It handles data blobs, image builds, and orchestration. You focus on tuning with libraries like Unsloth, or serving via vLLM...

Write Deep Learning Code Locally and Run on GPUs Instantly
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@faun shared a link, 1 month ago

The RAG Obituary: Killed by Agents, Buried by Context Windows

Agent-based setups are starting to edge out old-school RAG. As LLMs snag multi-million-token context windows and better task chops, the need for chunking, embeddings, and reranking starts to fade. Claude Code, for example, skips all that - with direct file access and smart navigation instead. Retrie..

The RAG Obituary: Killed by Agents, Buried by Context Windows
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@faun shared a link, 1 month ago

Serverless RL: Faster, Cheaper and More Flexible RL Training

New product, Serverless RL, available through collaboration between CoreWeave, Weights & Biases, and OpenPipe. Offers fast training, lower costs, and simple model deployment. Saves time with no infra setup, faster feedback loops, and easier entry into RL training...

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@faun shared a link, 1 month ago

Ansible Service Module: Start, Stop, & Manage Services

The Ansibleservicemodulehandles LinuxandWindows without choking on init system quirks. One playbook can start, stop, enable, or restart anything - no matter the OS. Idempotent, so you don’t have to babysit state. Clean and repeatable. Bonus: it’s great for wrangling fleets. Think: coordinating servi..

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@faun shared a link, 1 month ago

How LogSeam Searches 500 Million Logs per second

LogSeam rips through500M log searches/secand pushes1.5+ TB/s throughputusing Tigris’ geo-distributed object storage. It slashes log volume by 100× with Parquet + Zstandard compression. Then it spins up compute on the fly, right where the data lives—no long-running infrastructure, no laggy reads...

How LogSeam Searches 500 Million Logs per second
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@faun shared a link, 1 month ago

Automated GitHub Self-Hosted Runner Cleanup: Lambda Functions and Auto Scaling Lifecycle Hooks

When an EC2 instance in an Auto Scaling Group shuts down, event-driven plumbing kicks in. Alifecycle hookcatches the scale-in, fires off an SNS notification, and triggers aLambda. That Lambda calls the GitHub API to yank the self-hosted runner before the instance dies. No dangling runners. No manual..

Automated GitHub Self-Hosted Runner Cleanup: Lambda Functions and Auto Scaling Lifecycle Hooks
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@faun shared a link, 1 month ago

How We Migrated DB 1 to DB 2 , 1 Billion Records Without Downtime

A team movedover 1 billion production records- no downtime, no drama. The stack: dual writes, Kafka retries, and idempotent inserts to keep it clean. They ranshadow readsto sniff for errors, chunked the transfers with checksums, and held off indexing to keep inserts fast. Caches got warmed early to ..

How We Migrated DB 1 to DB 2 , 1 Billion Records Without Downtime
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